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While Deep Neural Networks (DNNs) have established the fundamentals ofDNN-based autonomous driving systems, they may exhibit erroneous behaviors andcause fatal accidents. To resolve the safety issues of autonomous drivingsystems, a recent set of testing techniques have been designed to automaticallygenerate test cases, e.g., new input images transformed from the original ones.Unfortunately, many such generated input images often render inferiorauthenticity, lacking accurate semantic information of the driving scenes andhence compromising the resulting efficacy and reliability.  In this paper, we propose DeepRoad, an unsupervised framework toautomatically generate large amounts of accurate driving scenes to test theconsistency of DNN-based autonomous driving systems across different scenes. Inparticular, DeepRoad delivers driving scenes with various weather conditions(including those with rather extreme conditions) by applying the GenerativeAdversarial Networks (GANs) along with the corresponding real-world weatherscenes. Moreover, we have implemented DeepRoad to test three well-recognizedDNN-based autonomous driving systems. Experimental results demonstrate thatDeepRoad can detect thousands of behavioral inconsistencies in these systems.

DeepRoad: GAN-based Metamorphic Autonomous Driving System Testing